CometKiwi：IST-Unbabel 2022提交质量评估共享任务
CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task
摘要：
我们介绍了IST和Unbabel对WMT 2022质量评估（QE）共享任务的联合贡献。我们的团队参与了所有三个子任务：（i）句子和单词级质量预测;（ii）可解释的QE;以及（iii）要害差错检测。对于所有的任务，我们都建立在COMET框架之上，将其与OpenKiwi的预测器-估计器架构连接起来，并为其配备了一个单词级的序列标记器和一个解释提取器。我们的研究结果表明，在预训练过程中加入参考文献可以提高下游任务中几种语言对的表现，并且与句子和单词级别的目标联合训练可以进一步提高性能。此外，将注意力和梯度信息相结合被证明是提取高级QE模型的良好解释的最佳策略。总体而言，我们提交的作品在几乎所有语言对的所有三项任务中都取得了相当大的成绩。
英文摘要：
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE). Our team participated on all three subtasks: (i) Sentence and Word-level Quality Prediction; (ii) Explainable QE; and (iii) Critical Error Detection. For all tasks we build on top of the COMET framework, connecting it with the predictor-estimator architecture of OpenKiwi, and equipping it with a word-level sequence tagger and an explanation extractor. Our results suggest that incorporating references during pretraining improves performance across several language pairs on downstream tasks, and that jointly training with sentence and word-level objectives yields a further boost. Furthermore, combining attention and gradient information proved to be the top strategy for extracting good explanations of sentence-level QE models. Overall, our submissions achieved the best results for all three tasks for almost all language pairs by a considerable margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
